An exciting emerging field of signal processing is the decoding of neural signals drawn directly from the brain. One of the goals pursued in the field is to restore function to patients with paralyzed limbs through a direct interface with the brain. This interface is also referred to as a brain machine interface. To achieve the goal of developing brain machine interfaces, a signal processing interface must be developed which decodes neural activity. The decoded neural signals could then be used as control signals to control a prosthetic device and restore function. For an overview of the state of this art, the reader is referred to references [R1-R3].
A typical approach to investigating neural coding of motor control has been to use microelectrodes to record the activity of an ensemble of neurons while also recording the related arm movements ([1–3]). Neural activity immediately preceding or simultaneous with arm movements is termed “peri-movement.” These signals are often highly correlated with electrically measured muscle activity, suggesting they correspond to muscle control signals. In some brain regions, there is also neural activity long before, or even without, actual movement. This is termed “plan” activity because of its association with intended movements. In the motor and pre-motor cortical regions of the brain, it is common to find neural activity of both types. Thus, in a situation where it is not possible to pre-select the type of neural activity, it is desirable to consider the optimal use of the data gathered, whether plan or peri-movement or both. One might anticipate that combining plan activity with peri-movement activity would improve the accuracy of the reconstructed end-point of the movement since the plan activity provides additional information as to where the movement should come to rest. Accordingly, the art is in need of new developments of decoding neural signals for movement control.